Simplify managed agents (#484)

* Simplify managed agents

docs/source/en/examples/multiagents.md

@@ -19,7 +19,7 @@ rendered properly in your Markdown viewer.
 
 In this notebook we will make a **multi-agent web browser: an agentic system with several agents collaborating to solve problems using the web!**
 
-It will be a simple hierarchy, using a `ManagedAgent` object to wrap the managed web search agent:
+It will be a simple hierarchy:
 
 ```
               +----------------+
@@ -28,15 +28,12 @@ It will be a simple hierarchy, using a `ManagedAgent` object to wrap the managed
                        |
         _______________|______________
        |                              |
-  Code interpreter   +--------------------------------+
+Code Interpreter            +------------------+
-       tool          |         Managed agent          |
+    tool                    | Web Search agent |
-                     |      +------------------+      |
+                            +------------------+
-                     |      | Web Search agent |      |
+                               |            |
-                     |      +------------------+      |
+                        Web Search tool     |
-                     |         |            |         |
+                                   Visit webpage tool
-                     |  Web Search tool     |         |
-                     |             Visit webpage tool |
-                     +--------------------------------+
 ```
 Let's set up this system. 
 
@@ -127,7 +124,6 @@ from smolagents import (
     CodeAgent,
     ToolCallingAgent,
     HfApiModel,
-    ManagedAgent,
     DuckDuckGoSearchTool,
     LiteLLMModel,
 )
@@ -138,20 +134,14 @@ web_agent = ToolCallingAgent(
     tools=[DuckDuckGoSearchTool(), visit_webpage],
     model=model,
     max_steps=10,
-)
-```
-
-We then wrap this agent into a `ManagedAgent` that will make it callable by its manager agent.
-
-```py
-managed_web_agent = ManagedAgent(
-    agent=web_agent,
     name="search",
     description="Runs web searches for you. Give it your query as an argument.",
 )
 ```
 
-Finally we create a manager agent, and upon initialization we pass our managed agent to it in its `managed_agents` argument.
+Note that we gave this agent attributes `name` and `description`, mandatory attributes to make this agent callable by its manager agent.
+
+Then we create a manager agent, and upon initialization we pass our managed agent to it in its `managed_agents` argument.
 
 Since this agent is the one tasked with the planning and thinking, advanced reasoning will be beneficial, so a `CodeAgent` will be the best choice.
 
@@ -161,7 +151,7 @@ Also, we want to ask a question that involves the current year and does addition
 manager_agent = CodeAgent(
     tools=[],
     model=model,
-    managed_agents=[managed_web_agent],
+    managed_agents=[web_agent],
     additional_authorized_imports=["time", "numpy", "pandas"],
 )
 ```

docs/source/en/reference/agents.md

@@ -45,7 +45,7 @@ Both require arguments `model` and list of tools `tools` at initialization.
 
 ### ManagedAgent
 
-[[autodoc]] ManagedAgent
+_This class is deprecated since 1.8.0: now you simply need to pass attributes `name` and `description` to a normal agent to make it callable by a manager agent._
 
 ### stream_to_gradio
 

docs/source/en/reference/models.md

@@ -61,7 +61,7 @@ from smolagents import TransformersModel
 
 model = TransformersModel(model_id="HuggingFaceTB/SmolLM-135M-Instruct")
 
-print(model([{"role": "user", "content": "Ok!"}], stop_sequences=["great"]))
+print(model([{"role": "user", "content": [{"type": "text", "text": "Ok!"}]}], stop_sequences=["great"]))
 ```
 ```text
 >>> What a
@@ -80,9 +80,7 @@ The `HfApiModel` wraps huggingface_hub's [InferenceClient](https://huggingface.c
 from smolagents import HfApiModel
 
 messages = [
-  {"role": "user", "content": "Hello, how are you?"},
+  {"role": "user", "content": [{"type": "text", "text": "Hello, how are you?"}]}
-  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
-  {"role": "user", "content": "No need to help, take it easy."},
 ]
 
 model = HfApiModel()
@@ -102,9 +100,7 @@ You can pass kwargs upon model initialization that will then be used whenever us
 from smolagents import LiteLLMModel
 
 messages = [
-  {"role": "user", "content": "Hello, how are you?"},
+  {"role": "user", "content": [{"type": "text", "text": "Hello, how are you?"}]}
-  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
-  {"role": "user", "content": "No need to help, take it easy."},
 ]
 
 model = LiteLLMModel("anthropic/claude-3-5-sonnet-latest", temperature=0.2, max_tokens=10)

docs/source/en/tutorials/inspect_runs.md

@@ -78,7 +78,6 @@ Then you can run your agents!
 from smolagents import (
     CodeAgent,
     ToolCallingAgent,
-    ManagedAgent,
     DuckDuckGoSearchTool,
     VisitWebpageTool,
     HfApiModel,
@@ -86,19 +85,17 @@ from smolagents import (
 
 model = HfApiModel()
 
-agent = ToolCallingAgent(
+search_agent = ToolCallingAgent(
     tools=[DuckDuckGoSearchTool(), VisitWebpageTool()],
     model=model,
-)
+    name="search_agent",
-managed_agent = ManagedAgent(
-    agent=agent,
-    name="managed_agent",
     description="This is an agent that can do web search.",
 )
+
 manager_agent = CodeAgent(
     tools=[],
     model=model,
-    managed_agents=[managed_agent],
+    managed_agents=[search_agent],
 )
 manager_agent.run(
     "If the US keeps its 2024 growth rate, how many years will it take for the GDP to double?"

docs/source/zh/reference/agents.md

@@ -47,7 +47,7 @@ Both require arguments `model` and list of tools `tools` at initialization.
 
 ### ManagedAgent
 
-[[autodoc]] ManagedAgent
+_This class is deprecated since 1.8.0: now you just need to pass name and description attributes to an agent to use it as a ManagedAgent._
 
 ### stream_to_gradio
 

examples/e2b_example.py

@@ -42,7 +42,7 @@ agent = CodeAgent(
 )
 
 agent.run(
-    "Return me an image of a cat. Directly use the image provided in your state.",
+    "Calculate how much is 2+2, then return me an image of a cat. Directly use the image provided in your state.",
     additional_args={"cat_image": get_cat_image()},
 )  # Asking to directly return the image from state tests that additional_args are properly sent to server.
 

examples/inspect_multiagent_run.py

@@ -7,7 +7,6 @@ from smolagents import (
     CodeAgent,
     DuckDuckGoSearchTool,
     HfApiModel,
-    ManagedAgent,
     ToolCallingAgent,
     VisitWebpageTool,
 )
@@ -23,18 +22,16 @@ SmolagentsInstrumentor().instrument(tracer_provider=trace_provider, skip_dep_che
 # Then we run the agentic part!
 model = HfApiModel()
 
-agent = ToolCallingAgent(
+search_agent = ToolCallingAgent(
     tools=[DuckDuckGoSearchTool(), VisitWebpageTool()],
     model=model,
-)
+    name="search_agent",
-managed_agent = ManagedAgent(
-    agent=agent,
-    name="managed_agent",
     description="This is an agent that can do web search.",
 )
+
 manager_agent = CodeAgent(
     tools=[],
     model=model,
-    managed_agents=[managed_agent],
+    managed_agents=[search_agent],
 )
 manager_agent.run("If the US keeps it 2024 growth rate, how many years would it take for the GDP to double?")

src/smolagents/agents.py

@@ -140,6 +140,9 @@ class MultiStepAgent:
         managed_agents (`list`, *optional*): Managed agents that the agent can call.
         step_callbacks (`list[Callable]`, *optional*): Callbacks that will be called at each step.
         planning_interval (`int`, *optional*): Interval at which the agent will run a planning step.
+        name (`str`, *optional*): Necessary for a managed agent only - the name by which this agent can be called.
+        description (`str`, *optional*): Necessary for a managed agent only - the description of this agent.
+        managed_agent_prompt (`str`, *optional*): Custom prompt for the managed agent. Defaults to None.
     """
 
     def __init__(
@@ -156,6 +159,9 @@ class MultiStepAgent:
         managed_agents: Optional[List] = None,
         step_callbacks: Optional[List[Callable]] = None,
         planning_interval: Optional[int] = None,
+        name: Optional[str] = None,
+        description: Optional[str] = None,
+        managed_agent_prompt: Optional[str] = None,
     ):
         if system_prompt is None:
             system_prompt = CODE_SYSTEM_PROMPT
@@ -172,9 +178,16 @@ class MultiStepAgent:
         self.grammar = grammar
         self.planning_interval = planning_interval
         self.state = {}
+        self.name = name
+        self.description = description
+        self.managed_agent_prompt = managed_agent_prompt if managed_agent_prompt else MANAGED_AGENT_PROMPT
 
         self.managed_agents = {}
         if managed_agents is not None:
+            for managed_agent in managed_agents:
+                assert managed_agent.name and managed_agent.description, (
+                    "All managed agents need both a name and a description!"
+                )
             self.managed_agents = {agent.name: agent for agent in managed_agents}
 
         for tool in tools:
@@ -638,6 +651,22 @@ Now begin!""",
         """
         self.memory.replay(self.logger, detailed=detailed)
 
+    def __call__(self, request, provide_run_summary=False, **kwargs):
+        """Adds additional prompting for the managed agent, and runs it."""
+        full_task = self.managed_agent_prompt.format(name=self.name, task=request).strip()
+        output = self.run(full_task, **kwargs)
+        if provide_run_summary:
+            answer = f"Here is the final answer from your managed agent '{self.name}':\n"
+            answer += str(output)
+            answer += f"\n\nFor more detail, find below a summary of this agent's work:\nSUMMARY OF WORK FROM AGENT '{self.name}':\n"
+            for message in self.write_memory_to_messages(summary_mode=True):
+                content = message["content"]
+                answer += "\n" + truncate_content(str(content)) + "\n---"
+            answer += f"\nEND OF SUMMARY OF WORK FROM AGENT '{self.name}'."
+            return answer
+        else:
+            return output
+
 
 class ToolCallingAgent(MultiStepAgent):
     """
@@ -896,7 +925,7 @@ class CodeAgent(MultiStepAgent):
                 ]
             observation = "Execution logs:\n" + execution_logs
         except Exception as e:
-            if "print_outputs" in self.python_executor.state:
+            if hasattr(self.python_executor, "state") and "print_outputs" in self.python_executor.state:
                 execution_logs = self.python_executor.state["print_outputs"]
                 if len(execution_logs) > 0:
                     execution_outputs_console = [
@@ -928,59 +957,4 @@ class CodeAgent(MultiStepAgent):
         return output if is_final_answer else None
 
 
-class ManagedAgent:
+__all__ = ["MultiStepAgent", "CodeAgent", "ToolCallingAgent", "AgentMemory"]
-    """
-    ManagedAgent class that manages an agent and provides additional prompting and run summaries.
-
-    Args:
-        agent (`object`): The agent to be managed.
-        name (`str`): The name of the managed agent.
-        description (`str`): A description of the managed agent.
-        additional_prompting (`Optional[str]`, *optional*): Additional prompting for the managed agent. Defaults to None.
-        provide_run_summary (`bool`, *optional*): Whether to provide a run summary after the agent completes its task. Defaults to False.
-        managed_agent_prompt (`Optional[str]`, *optional*): Custom prompt for the managed agent. Defaults to None.
-
-    """
-
-    def __init__(
-        self,
-        agent,
-        name,
-        description,
-        additional_prompting: Optional[str] = None,
-        provide_run_summary: bool = False,
-        managed_agent_prompt: Optional[str] = None,
-    ):
-        self.agent = agent
-        self.name = name
-        self.description = description
-        self.additional_prompting = additional_prompting
-        self.provide_run_summary = provide_run_summary
-        self.managed_agent_prompt = managed_agent_prompt if managed_agent_prompt else MANAGED_AGENT_PROMPT
-
-    def write_full_task(self, task):
-        """Adds additional prompting for the managed agent, like 'add more detail in your answer'."""
-        full_task = self.managed_agent_prompt.format(name=self.name, task=task)
-        if self.additional_prompting:
-            full_task = full_task.replace("\n{additional_prompting}", self.additional_prompting).strip()
-        else:
-            full_task = full_task.replace("\n{additional_prompting}", "").strip()
-        return full_task
-
-    def __call__(self, request, **kwargs):
-        full_task = self.write_full_task(request)
-        output = self.agent.run(full_task, **kwargs)
-        if self.provide_run_summary:
-            answer = f"Here is the final answer from your managed agent '{self.name}':\n"
-            answer += str(output)
-            answer += f"\n\nFor more detail, find below a summary of this agent's work:\nSUMMARY OF WORK FROM AGENT '{self.name}':\n"
-            for message in self.agent.write_memory_to_messages(summary_mode=True):
-                content = message["content"]
-                answer += "\n" + truncate_content(str(content)) + "\n---"
-            answer += f"\nEND OF SUMMARY OF WORK FROM AGENT '{self.name}'."
-            return answer
-        else:
-            return output
-
-
-__all__ = ["ManagedAgent", "MultiStepAgent", "CodeAgent", "ToolCallingAgent", "AgentMemory"]

src/smolagents/e2b_executor.py

@@ -45,9 +45,11 @@ class E2BExecutor:
                 """Please install 'e2b' extra to use E2BExecutor: `pip install "smolagents[e2b]"`"""
             )
 
+        self.logger.log("Initializing E2B executor, hold on...")
+
         self.custom_tools = {}
         self.final_answer = False
-        self.final_answer_pattern = re.compile(r"^final_answer\((.*)\)$")
+        self.final_answer_pattern = re.compile(r"final_answer\((.*?)\)")
         self.sbx = Sandbox()  # "qywp2ctmu2q7jzprcf4j")
         # TODO: validate installing agents package or not
         # print("Installing agents package on remote executor...")
@@ -90,7 +92,7 @@ class E2BExecutor:
         self.logger.log(tool_definition_execution.logs)
 
     def run_code_raise_errors(self, code: str):
-        if self.final_answer_pattern.match(code):
+        if self.final_answer_pattern.search(code) is not None:
             self.final_answer = True
         execution = self.sbx.run_code(
             code,
@@ -152,7 +154,9 @@ locals().update({key: value for key, value in pickle_dict.items()})
                     ]:
                         if getattr(result, attribute_name) is not None:
                             return getattr(result, attribute_name), execution_logs, self.final_answer
-            raise ValueError("No main result returned by executor!")
+            if self.final_answer:
+                raise ValueError("No main result returned by executor!")
+            return None, execution_logs, False
 
 
 __all__ = ["E2BExecutor"]

src/smolagents/prompts.py

@@ -510,7 +510,7 @@ Your final_answer WILL HAVE to contain these parts:
 
 Put all these in your final_answer tool, everything that you do not pass as an argument to final_answer will be lost.
 And even if your task resolution is not successful, please return as much context as possible, so that your manager can act upon this feedback.
-{{additional_prompting}}"""
+"""
 
 __all__ = [
     "USER_PROMPT_PLAN_UPDATE",

tests/test_agents.py

@@ -25,7 +25,6 @@ from smolagents.agent_types import AgentImage, AgentText
 from smolagents.agents import (
     AgentMaxStepsError,
     CodeAgent,
-    ManagedAgent,
     MultiStepAgent,
     ToolCall,
     ToolCallingAgent,
@@ -465,22 +464,20 @@ class AgentTests(unittest.TestCase):
         assert res[0] == 0.5
 
     def test_init_managed_agent(self):
-        agent = CodeAgent(tools=[], model=fake_code_functiondef)
+        agent = CodeAgent(tools=[], model=fake_code_functiondef, name="managed_agent", description="Empty")
-        managed_agent = ManagedAgent(agent, name="managed_agent", description="Empty")
+        assert agent.name == "managed_agent"
-        assert managed_agent.name == "managed_agent"
+        assert agent.description == "Empty"
-        assert managed_agent.description == "Empty"
 
     def test_agent_description_gets_correctly_inserted_in_system_prompt(self):
-        agent = CodeAgent(tools=[], model=fake_code_functiondef)
+        managed_agent = CodeAgent(tools=[], model=fake_code_functiondef, name="managed_agent", description="Empty")
-        managed_agent = ManagedAgent(agent, name="managed_agent", description="Empty")
         manager_agent = CodeAgent(
             tools=[],
             model=fake_code_functiondef,
             managed_agents=[managed_agent],
         )
-        assert "You can also give requests to team members." not in agent.system_prompt
+        assert "You can also give requests to team members." not in managed_agent.system_prompt
         print("ok1")
-        assert "{{managed_agents_descriptions}}" not in agent.system_prompt
+        assert "{{managed_agents_descriptions}}" not in managed_agent.system_prompt
         assert "You can also give requests to team members." in manager_agent.system_prompt
 
     def test_code_agent_missing_import_triggers_advice_in_error_log(self):
@@ -587,10 +584,6 @@ final_answer("Final report.")
             tools=[],
             model=managed_model,
             max_steps=10,
-        )
-
-        managed_web_agent = ManagedAgent(
-            agent=web_agent,
             name="search_agent",
             description="Runs web searches for you. Give it your request as an argument. Make the request as detailed as needed, you can ask for thorough reports",
         )
@@ -598,7 +591,7 @@ final_answer("Final report.")
         manager_code_agent = CodeAgent(
             tools=[],
             model=manager_model,
-            managed_agents=[managed_web_agent],
+            managed_agents=[web_agent],
             additional_authorized_imports=["time", "numpy", "pandas"],
         )
 
@@ -608,7 +601,7 @@ final_answer("Final report.")
         manager_toolcalling_agent = ToolCallingAgent(
             tools=[],
             model=manager_model,
-            managed_agents=[managed_web_agent],
+            managed_agents=[web_agent],
         )
 
         report = manager_toolcalling_agent.run("Fake question.")